Initial open-source release
PiperOrigin-RevId: 271685289
diff --git a/test/average-pooling-operator-tester.h b/test/average-pooling-operator-tester.h
new file mode 100644
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--- /dev/null
+++ b/test/average-pooling-operator-tester.h
@@ -0,0 +1,899 @@
+// Copyright (c) Facebook, Inc. and its affiliates.
+// All rights reserved.
+//
+// Copyright 2019 Google LLC
+//
+// This source code is licensed under the BSD-style license found in the
+// LICENSE file in the root directory of this source tree.
+
+#pragma once
+
+#include <gtest/gtest.h>
+
+#include <algorithm>
+#include <cmath>
+#include <cassert>
+#include <cstddef>
+#include <cstdlib>
+#include <functional>
+#include <random>
+#include <vector>
+
+#include <xnnpack.h>
+
+
+class AveragePoolingOperatorTester {
+ public:
+ inline AveragePoolingOperatorTester& padding(uint32_t padding) {
+ this->padding_top_ = padding;
+ this->padding_right_ = padding;
+ this->padding_bottom_ = padding;
+ this->padding_left_ = padding;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& padding(uint32_t padding_height, uint32_t padding_width) {
+ this->padding_top_ = padding_height;
+ this->padding_right_ = padding_width;
+ this->padding_bottom_ = padding_height;
+ this->padding_left_ = padding_width;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& padding_height(uint32_t padding_height) {
+ this->padding_top_ = padding_height;
+ this->padding_bottom_ = padding_height;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& padding_width(uint32_t padding_width) {
+ this->padding_right_ = padding_width;
+ this->padding_left_ = padding_width;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& padding_top(uint32_t padding_top) {
+ this->padding_top_ = padding_top;
+ return *this;
+ }
+
+ inline uint32_t padding_top() const {
+ return this->padding_top_;
+ }
+
+ inline AveragePoolingOperatorTester& padding_right(uint32_t padding_right) {
+ this->padding_right_ = padding_right;
+ return *this;
+ }
+
+ inline uint32_t padding_right() const {
+ return this->padding_right_;
+ }
+
+ inline AveragePoolingOperatorTester& padding_bottom(uint32_t padding_bottom) {
+ this->padding_bottom_ = padding_bottom;
+ return *this;
+ }
+
+ inline uint32_t padding_bottom() const {
+ return this->padding_bottom_;
+ }
+
+ inline AveragePoolingOperatorTester& padding_left(uint32_t padding_left) {
+ this->padding_left_ = padding_left;
+ return *this;
+ }
+
+ inline uint32_t padding_left() const {
+ return this->padding_left_;
+ }
+
+ inline AveragePoolingOperatorTester& input_size(size_t input_height, size_t input_width) {
+ assert(input_height >= 1);
+ assert(input_width >= 1);
+ this->input_height_ = input_height;
+ this->input_width_ = input_width;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& input_height(size_t input_height) {
+ assert(input_height >= 1);
+ this->input_height_ = input_height;
+ return *this;
+ }
+
+ inline size_t input_height() const {
+ return this->input_height_;
+ }
+
+ inline AveragePoolingOperatorTester& input_width(size_t input_width) {
+ assert(input_width >= 1);
+ this->input_width_ = input_width;
+ return *this;
+ }
+
+ inline size_t input_width() const {
+ return this->input_width_;
+ }
+
+ inline AveragePoolingOperatorTester& channels(size_t channels) {
+ assert(channels != 0);
+ this->channels_ = channels;
+ return *this;
+ }
+
+ inline size_t channels() const {
+ return this->channels_;
+ }
+
+ inline AveragePoolingOperatorTester& batch_size(size_t batch_size) {
+ assert(batch_size != 0);
+ this->batch_size_ = batch_size;
+ return *this;
+ }
+
+ inline size_t batch_size() const {
+ return this->batch_size_;
+ }
+
+ inline AveragePoolingOperatorTester& pooling_size(uint32_t pooling_size) {
+ assert(pooling_size >= 1);
+ this->pooling_height_ = pooling_size;
+ this->pooling_width_ = pooling_size;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& pooling_size(uint32_t pooling_height, uint32_t pooling_width) {
+ assert(pooling_height >= 1);
+ assert(pooling_width >= 1);
+ this->pooling_height_ = pooling_height;
+ this->pooling_width_ = pooling_width;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& pooling_height(uint32_t pooling_height) {
+ assert(pooling_height >= 1);
+ this->pooling_height_ = pooling_height;
+ return *this;
+ }
+
+ inline uint32_t pooling_height() const {
+ return this->pooling_height_;
+ }
+
+ inline AveragePoolingOperatorTester& pooling_width(uint32_t pooling_width) {
+ assert(pooling_width >= 1);
+ this->pooling_width_ = pooling_width;
+ return *this;
+ }
+
+ inline uint32_t pooling_width() const {
+ return this->pooling_width_;
+ }
+
+ inline AveragePoolingOperatorTester& stride(uint32_t stride) {
+ assert(stride >= 1);
+ this->stride_height_ = stride;
+ this->stride_width_ = stride;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& stride(uint32_t stride_height, uint32_t stride_width) {
+ assert(stride_height >= 1);
+ assert(stride_width >= 1);
+ this->stride_height_ = stride_height;
+ this->stride_width_ = stride_width;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& stride_height(uint32_t stride_height) {
+ assert(stride_height >= 1);
+ this->stride_height_ = stride_height;
+ return *this;
+ }
+
+ inline uint32_t stride_height() const {
+ return this->stride_height_;
+ }
+
+ inline AveragePoolingOperatorTester& stride_width(uint32_t stride_width) {
+ assert(stride_width >= 1);
+ this->stride_width_ = stride_width;
+ return *this;
+ }
+
+ inline uint32_t stride_width() const {
+ return this->stride_width_;
+ }
+
+ inline size_t output_height() const {
+ const size_t padded_input_height = padding_top() + input_height() + padding_bottom();
+ if (padded_input_height <= pooling_height()) {
+ return 1;
+ } else {
+ return (padded_input_height - pooling_height()) / stride_height() + 1;
+ }
+ }
+
+ inline size_t output_width() const {
+ const size_t padded_input_width = padding_left() + input_width() + padding_right();
+ if (padded_input_width <= pooling_width()) {
+ return 1;
+ } else {
+ return (padded_input_width - pooling_width()) / stride_width() + 1;
+ }
+ }
+
+ inline AveragePoolingOperatorTester& input_pixel_stride(size_t input_pixel_stride) {
+ assert(input_pixel_stride != 0);
+ this->input_pixel_stride_ = input_pixel_stride;
+ return *this;
+ }
+
+ inline size_t input_pixel_stride() const {
+ if (this->input_pixel_stride_ == 0) {
+ return channels();
+ } else {
+ assert(this->input_pixel_stride_ >= channels());
+ return this->input_pixel_stride_;
+ }
+ }
+
+ inline AveragePoolingOperatorTester& output_pixel_stride(size_t output_pixel_stride) {
+ assert(output_pixel_stride != 0);
+ this->output_pixel_stride_ = output_pixel_stride;
+ return *this;
+ }
+
+ inline size_t output_pixel_stride() const {
+ if (this->output_pixel_stride_ == 0) {
+ return channels();
+ } else {
+ assert(this->output_pixel_stride_ >= channels());
+ return this->output_pixel_stride_;
+ }
+ }
+
+ inline AveragePoolingOperatorTester& next_input_size(uint32_t next_input_height, uint32_t next_input_width) {
+ assert(next_input_height >= 1);
+ assert(next_input_width >= 1);
+ this->next_input_height_ = next_input_height;
+ this->next_input_width_ = next_input_width;
+ return *this;
+ }
+
+ inline AveragePoolingOperatorTester& next_input_height(uint32_t next_input_height) {
+ assert(next_input_height >= 1);
+ this->next_input_height_ = next_input_height;
+ return *this;
+ }
+
+ inline uint32_t next_input_height() const {
+ if (this->next_input_height_ == 0) {
+ return input_height();
+ } else {
+ return this->next_input_height_;
+ }
+ }
+
+ inline AveragePoolingOperatorTester& next_input_width(uint32_t next_input_width) {
+ assert(next_input_width >= 1);
+ this->next_input_width_ = next_input_width;
+ return *this;
+ }
+
+ inline uint32_t next_input_width() const {
+ if (this->next_input_width_ == 0) {
+ return input_width();
+ } else {
+ return this->next_input_width_;
+ }
+ }
+
+ inline size_t next_output_height() const {
+ const size_t padded_next_input_height = padding_top() + next_input_height() + padding_bottom();
+ if (padded_next_input_height <= pooling_height()) {
+ return 1;
+ } else {
+ return (padded_next_input_height - pooling_height()) / stride_height() + 1;
+ }
+ }
+
+ inline size_t next_output_width() const {
+ const size_t padded_next_input_width = padding_left() + next_input_width() + padding_right();
+ if (padded_next_input_width <= pooling_width()) {
+ return 1;
+ } else {
+ return (padded_next_input_width - pooling_width()) / stride_width() + 1;
+ }
+ }
+
+ inline AveragePoolingOperatorTester& next_batch_size(size_t next_batch_size) {
+ assert(next_batch_size >= 1);
+ this->next_batch_size_ = next_batch_size;
+ return *this;
+ }
+
+ inline size_t next_batch_size() const {
+ if (this->next_batch_size_ == 0) {
+ return batch_size();
+ } else {
+ return this->next_batch_size_;
+ }
+ }
+
+ inline AveragePoolingOperatorTester& input_scale(float input_scale) {
+ assert(input_scale > 0.0f);
+ assert(std::isnormal(input_scale));
+ this->input_scale_ = input_scale;
+ return *this;
+ }
+
+ inline float input_scale() const {
+ return this->input_scale_;
+ }
+
+ inline AveragePoolingOperatorTester& input_zero_point(uint8_t input_zero_point) {
+ this->input_zero_point_ = input_zero_point;
+ return *this;
+ }
+
+ inline uint8_t input_zero_point() const {
+ return this->input_zero_point_;
+ }
+
+ inline AveragePoolingOperatorTester& output_scale(float output_scale) {
+ assert(output_scale > 0.0f);
+ assert(std::isnormal(output_scale));
+ this->output_scale_ = output_scale;
+ return *this;
+ }
+
+ inline float output_scale() const {
+ return this->output_scale_;
+ }
+
+ inline AveragePoolingOperatorTester& output_zero_point(uint8_t output_zero_point) {
+ this->output_zero_point_ = output_zero_point;
+ return *this;
+ }
+
+ inline uint8_t output_zero_point() const {
+ return this->output_zero_point_;
+ }
+
+ inline AveragePoolingOperatorTester& qmin(uint8_t qmin) {
+ this->qmin_ = qmin;
+ return *this;
+ }
+
+ inline uint8_t qmin() const {
+ return this->qmin_;
+ }
+
+ inline AveragePoolingOperatorTester& qmax(uint8_t qmax) {
+ this->qmax_ = qmax;
+ return *this;
+ }
+
+ inline uint8_t qmax() const {
+ return this->qmax_;
+ }
+
+ inline AveragePoolingOperatorTester& iterations(size_t iterations) {
+ this->iterations_ = iterations;
+ return *this;
+ }
+
+ inline size_t iterations() const {
+ return this->iterations_;
+ }
+
+ void TestQ8() const {
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
+
+ std::vector<uint8_t> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(uint8_t));
+ std::vector<uint8_t> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
+ std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
+ for (size_t iteration = 0; iteration < iterations(); iteration++) {
+ std::generate(input.begin(), input.end(), std::ref(u8rng));
+ std::fill(output.begin(), output.end(), 0xA5);
+
+ // Compute reference results.
+ const double scale = double(input_scale()) / (double(output_scale()) * double(pooling_height() * pooling_width()));
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t oy = 0; oy < output_height(); oy++) {
+ for (size_t ox = 0; ox < output_width(); ox++) {
+ for (size_t c = 0; c < channels(); c++) {
+ double acc = 0.0f;
+ for (size_t py = 0; py < pooling_height(); py++) {
+ const size_t iy = oy * stride_height() + py - padding_top();
+ for (size_t px = 0; px < pooling_width(); px++) {
+ const size_t ix = ox * stride_width() + px - padding_left();
+ if (ix < input_width() && iy < input_height()) {
+ acc += double(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]) - int32_t(input_zero_point()));
+ }
+ }
+ }
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = float(acc * scale + double(output_zero_point()));
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
+ std::min<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmax()));
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
+ std::max<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmin()));
+ }
+ }
+ }
+ }
+
+ // Create, setup, run, and destroy Average Pooling operator.
+ ASSERT_EQ(xnn_status_success, xnn_initialize());
+ xnn_operator_t average_pooling_op = nullptr;
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_create_average_pooling2d_nhwc_q8(
+ padding_top(), padding_right(), padding_bottom(), padding_left(),
+ pooling_height(), pooling_width(),
+ stride_height(), stride_width(),
+ channels(), input_pixel_stride(), output_pixel_stride(),
+ input_zero_point(), input_scale(),
+ output_zero_point(), output_scale(),
+ qmin(), qmax(),
+ 0, &average_pooling_op));
+ ASSERT_NE(nullptr, average_pooling_op);
+
+ // Smart pointer to automatically delete average_pooling_op.
+ std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_average_pooling_op(average_pooling_op, xnn_delete_operator);
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_average_pooling2d_nhwc_q8(
+ average_pooling_op,
+ batch_size(), input_height(), input_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(average_pooling_op, nullptr /* thread pool */));
+
+ // Verify results.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t y = 0; y < output_height(); y++) {
+ for (size_t x = 0; x < output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_LE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax()));
+ ASSERT_GE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin()));
+ ASSERT_NEAR(float(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])),
+ output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], 0.80f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+ }
+ }
+
+ void TestF32() const {
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
+
+ std::vector<float> input((batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels() + XNN_EXTRA_BYTES / sizeof(float));
+ std::vector<float> output((batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels());
+ std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
+ for (size_t iteration = 0; iteration < iterations(); iteration++) {
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ std::fill(output.begin(), output.end(), std::nanf(""));
+
+ // Compute reference results, without clamping.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t oy = 0; oy < output_height(); oy++) {
+ for (size_t ox = 0; ox < output_width(); ox++) {
+ for (size_t c = 0; c < channels(); c++) {
+ float acc = 0.0f;
+ int32_t n = 0;
+ for (size_t py = 0; py < pooling_height(); py++) {
+ const size_t iy = oy * stride_height() + py - padding_top();
+ for (size_t px = 0; px < pooling_width(); px++) {
+ const size_t ix = ox * stride_width() + px - padding_left();
+ if (ix < input_width() && iy < input_height()) {
+ acc += input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
+ n += 1;
+ }
+ }
+ }
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = acc / float(n);
+ }
+ }
+ }
+ }
+
+ // Compute clamping parameters.
+ const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
+ const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
+ const float accumulated_range = accumulated_max - accumulated_min;
+ const float output_min = accumulated_range == 0.0f ?
+ -std::numeric_limits<float>::infinity() :
+ accumulated_min + accumulated_range / 255.0f * float(qmin());
+ const float output_max = accumulated_range == 0.0f ?
+ +std::numeric_limits<float>::infinity() :
+ accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
+
+ // Clamp reference results.
+ for (float& value : output_ref) {
+ value = std::max(std::min(value, output_max), output_min);
+ }
+
+ // Create, setup, run, and destroy Average Pooling operator.
+ ASSERT_EQ(xnn_status_success, xnn_initialize());
+ xnn_operator_t average_pooling_op = nullptr;
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_create_average_pooling2d_nhwc_f32(
+ padding_top(), padding_right(), padding_bottom(), padding_left(),
+ pooling_height(), pooling_width(),
+ stride_height(), stride_width(),
+ channels(), input_pixel_stride(), output_pixel_stride(),
+ output_min, output_max,
+ 0, &average_pooling_op));
+ ASSERT_NE(nullptr, average_pooling_op);
+
+ // Smart pointer to automatically delete average_pooling_op.
+ std::unique_ptr<xnn_operator, decltype(&xnn_delete_operator)> auto_average_pooling_op(average_pooling_op, xnn_delete_operator);
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_average_pooling2d_nhwc_f32(
+ average_pooling_op,
+ batch_size(), input_height(), input_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(average_pooling_op, nullptr /* thread pool */));
+
+ // Verify results.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t y = 0; y < output_height(); y++) {
+ for (size_t x = 0; x < output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max);
+ ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min);
+ ASSERT_NEAR(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
+ output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
+ std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-6f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+ }
+ }
+
+ void TestSetupQ8() const {
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto u8rng = std::bind(std::uniform_int_distribution<uint8_t>(), rng);
+
+ std::vector<uint8_t> input(XNN_EXTRA_BYTES / sizeof(uint8_t) + std::max(
+ (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(),
+ (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels()));
+ std::vector<uint8_t> output(std::max(
+ (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(),
+ (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels()));
+ std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
+ std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
+ for (size_t iteration = 0; iteration < iterations(); iteration++) {
+ std::generate(input.begin(), input.end(), std::ref(u8rng));
+ std::fill(output.begin(), output.end(), 0xA5);
+
+ // Compute reference results.
+ const double scale = double(input_scale()) / (double(output_scale()) * double(pooling_height() * pooling_width()));
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t oy = 0; oy < output_height(); oy++) {
+ for (size_t ox = 0; ox < output_width(); ox++) {
+ for (size_t c = 0; c < channels(); c++) {
+ double acc = 0.0f;
+ for (size_t py = 0; py < pooling_height(); py++) {
+ const size_t iy = oy * stride_height() + py - padding_top();
+ for (size_t px = 0; px < pooling_width(); px++) {
+ const size_t ix = ox * stride_width() + px - padding_left();
+ if (ix < input_width() && iy < input_height()) {
+ acc += double(int32_t(input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c]) - int32_t(input_zero_point()));
+ }
+ }
+ }
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = float(acc * scale + double(output_zero_point()));
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
+ std::min<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmax()));
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] =
+ std::max<float>(output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c], float(qmin()));
+ }
+ }
+ }
+ }
+
+ // Create, setup, and run Average Pooling operator once.
+ ASSERT_EQ(xnn_status_success, xnn_initialize());
+ xnn_operator_t average_pooling_op = nullptr;
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_create_average_pooling2d_nhwc_q8(
+ padding_top(), padding_right(), padding_bottom(), padding_left(),
+ pooling_height(), pooling_width(),
+ stride_height(), stride_width(),
+ channels(), input_pixel_stride(), output_pixel_stride(),
+ input_zero_point(), input_scale(),
+ output_zero_point(), output_scale(),
+ qmin(), qmax(),
+ 0, &average_pooling_op));
+ ASSERT_NE(nullptr, average_pooling_op);
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_average_pooling2d_nhwc_q8(
+ average_pooling_op,
+ batch_size(), input_height(), input_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(average_pooling_op, nullptr /* thread pool */));
+
+ // Verify results of the first run.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t y = 0; y < output_height(); y++) {
+ for (size_t x = 0; x < output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_LE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax()));
+ ASSERT_GE(uint32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin()));
+ ASSERT_NEAR(float(int32_t(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c])),
+ output_ref[((i * output_height() + y) * output_width() + x) * channels() + c], 0.80f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+
+ // Re-generate data for the second run.
+ std::generate(input.begin(), input.end(), std::ref(u8rng));
+ std::fill(output.begin(), output.end(), 0xA5);
+
+ // Compute reference results for the second run.
+ for (size_t i = 0; i < next_batch_size(); i++) {
+ for (size_t oy = 0; oy < next_output_height(); oy++) {
+ for (size_t ox = 0; ox < next_output_width(); ox++) {
+ for (size_t c = 0; c < channels(); c++) {
+ double acc = 0.0f;
+ for (size_t py = 0; py < pooling_height(); py++) {
+ const size_t iy = oy * stride_height() + py - padding_top();
+ for (size_t px = 0; px < pooling_width(); px++) {
+ const size_t ix = ox * stride_width() + px - padding_left();
+ if (ix < next_input_width() && iy < next_input_height()) {
+ acc += double(int32_t(input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c]) - int32_t(input_zero_point()));
+ }
+ }
+ }
+ next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] = float(acc * scale + double(output_zero_point()));
+ next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] =
+ std::min<float>(next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c], float(qmax()));
+ next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] =
+ std::max<float>(next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c], float(qmin()));
+ }
+ }
+ }
+ }
+
+ // Setup and run Average Pooling operator the second time, and destroy the operator.
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_average_pooling2d_nhwc_q8(
+ average_pooling_op,
+ next_batch_size(), next_input_height(), next_input_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(average_pooling_op, nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_delete_operator(average_pooling_op));
+ average_pooling_op = nullptr;
+
+ // Verify results of the second run.
+ for (size_t i = 0; i < next_batch_size(); i++) {
+ for (size_t y = 0; y < next_output_height(); y++) {
+ for (size_t x = 0; x < next_output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_LE(uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), uint32_t(qmax()));
+ ASSERT_GE(uint32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c]), uint32_t(qmin()));
+ ASSERT_NEAR(float(int32_t(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c])),
+ next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c], 0.80f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+ }
+ }
+
+ void TestSetupF32() const {
+ std::random_device random_device;
+ auto rng = std::mt19937(random_device());
+ auto f32rng = std::bind(std::uniform_real_distribution<float>(), rng);
+
+ std::vector<float> input(XNN_EXTRA_BYTES / sizeof(float) + std::max(
+ (batch_size() * input_height() * input_width() - 1) * input_pixel_stride() + channels(),
+ (next_batch_size() * next_input_height() * next_input_width() - 1) * input_pixel_stride() + channels()));
+ std::vector<float> output(std::max(
+ (batch_size() * output_height() * output_width() - 1) * output_pixel_stride() + channels(),
+ (next_batch_size() * next_output_height() * next_output_width() - 1) * output_pixel_stride() + channels()));
+ std::vector<float> output_ref(batch_size() * output_height() * output_width() * channels());
+ std::vector<float> next_output_ref(next_batch_size() * next_output_height() * next_output_width() * channels());
+ for (size_t iteration = 0; iteration < iterations(); iteration++) {
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ std::fill(output.begin(), output.end(), std::nanf(""));
+
+ // Compute reference results, without clamping.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t oy = 0; oy < output_height(); oy++) {
+ for (size_t ox = 0; ox < output_width(); ox++) {
+ for (size_t c = 0; c < channels(); c++) {
+ float acc = 0.0f;
+ size_t n = 0;
+ for (size_t py = 0; py < pooling_height(); py++) {
+ const size_t iy = oy * stride_height() + py - padding_top();
+ for (size_t px = 0; px < pooling_width(); px++) {
+ const size_t ix = ox * stride_width() + px - padding_left();
+ if (ix < input_width() && iy < input_height()) {
+ acc += input[((i * input_height() + iy) * input_width() + ix) * input_pixel_stride() + c];
+ n += 1;
+ }
+ }
+ }
+ output_ref[((i * output_height() + oy) * output_width() + ox) * channels() + c] = acc / float(n);
+ }
+ }
+ }
+ }
+
+ // Compute clamping parameters.
+ const float accumulated_min = *std::min_element(output_ref.cbegin(), output_ref.cend());
+ const float accumulated_max = *std::max_element(output_ref.cbegin(), output_ref.cend());
+ const float accumulated_range = accumulated_max - accumulated_min;
+ const float output_min = accumulated_range == 0.0f ?
+ -std::numeric_limits<float>::infinity() :
+ accumulated_min + accumulated_range / 255.0f * float(qmin());
+ const float output_max = accumulated_range == 0.0f ?
+ +std::numeric_limits<float>::infinity() :
+ accumulated_max - accumulated_range / 255.0f * float(255 - qmax());
+
+ // Clamp reference results.
+ for (float& value : output_ref) {
+ value = std::max(std::min(value, output_max), output_min);
+ }
+
+ // Create, setup, and run Average Pooling operator once.
+ ASSERT_EQ(xnn_status_success, xnn_initialize());
+ xnn_operator_t average_pooling_op = nullptr;
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_create_average_pooling2d_nhwc_f32(
+ padding_top(), padding_right(), padding_bottom(), padding_left(),
+ pooling_height(), pooling_width(),
+ stride_height(), stride_width(),
+ channels(), input_pixel_stride(), output_pixel_stride(),
+ output_min, output_max,
+ 0, &average_pooling_op));
+ ASSERT_NE(nullptr, average_pooling_op);
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_average_pooling2d_nhwc_f32(
+ average_pooling_op,
+ batch_size(), input_height(), input_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(average_pooling_op, nullptr /* thread pool */));
+
+ // Verify results of the first run.
+ for (size_t i = 0; i < batch_size(); i++) {
+ for (size_t y = 0; y < output_height(); y++) {
+ for (size_t x = 0; x < output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_LE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_max);
+ ASSERT_GE(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c], output_min);
+ ASSERT_NEAR(output[((i * output_height() + y) * output_width() + x) * output_pixel_stride() + c],
+ output_ref[((i * output_height() + y) * output_width() + x) * channels() + c],
+ std::abs(output_ref[((i * output_height() + y) * output_width() + x) * channels() + c]) * 1.0e-6f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+
+ // Re-generate data for the second run.
+ std::generate(input.begin(), input.end(), std::ref(f32rng));
+ std::fill(output.begin(), output.end(), std::nanf(""));
+
+ // Compute reference results for the second run.
+ for (size_t i = 0; i < next_batch_size(); i++) {
+ for (size_t oy = 0; oy < next_output_height(); oy++) {
+ for (size_t ox = 0; ox < next_output_width(); ox++) {
+ for (size_t c = 0; c < channels(); c++) {
+ float acc = 0.0f;
+ int32_t n = 0;
+ for (size_t py = 0; py < pooling_height(); py++) {
+ const size_t iy = oy * stride_height() + py - padding_top();
+ for (size_t px = 0; px < pooling_width(); px++) {
+ const size_t ix = ox * stride_width() + px - padding_left();
+ if (ix < next_input_width() && iy < next_input_height()) {
+ acc += input[((i * next_input_height() + iy) * next_input_width() + ix) * input_pixel_stride() + c];
+ n += 1;
+ }
+ }
+ }
+ next_output_ref[((i * next_output_height() + oy) * next_output_width() + ox) * channels() + c] =
+ std::max(std::min(acc / float(n), output_max), output_min);
+ }
+ }
+ }
+ }
+
+ // Setup and run Average Pooling operator the second time, and destroy the operator.
+ ASSERT_EQ(xnn_status_success,
+ xnn_setup_average_pooling2d_nhwc_f32(
+ average_pooling_op,
+ next_batch_size(), next_input_height(), next_input_width(),
+ input.data(), output.data(),
+ nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_run_operator(average_pooling_op, nullptr /* thread pool */));
+
+ ASSERT_EQ(xnn_status_success,
+ xnn_delete_operator(average_pooling_op));
+ average_pooling_op = nullptr;
+
+ // Verify results of the second run.
+ for (size_t i = 0; i < next_batch_size(); i++) {
+ for (size_t y = 0; y < next_output_height(); y++) {
+ for (size_t x = 0; x < next_output_width(); x++) {
+ for (size_t c = 0; c < channels(); c++) {
+ ASSERT_LE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_max);
+ ASSERT_GE(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c], output_min);
+ ASSERT_NEAR(output[((i * next_output_height() + y) * next_output_width() + x) * output_pixel_stride() + c],
+ next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c],
+ std::abs(next_output_ref[((i * next_output_height() + y) * next_output_width() + x) * channels() + c]) * 1.0e-6f) <<
+ "in batch index " << i << ", pixel (" << y << ", " << x << "), channel " << c;
+ }
+ }
+ }
+ }
+ }
+ }
+
+ private:
+ uint32_t padding_top_{0};
+ uint32_t padding_right_{0};
+ uint32_t padding_bottom_{0};
+ uint32_t padding_left_{0};
+ size_t input_height_{1};
+ size_t input_width_{1};
+ size_t channels_{1};
+ size_t batch_size_{1};
+ size_t input_pixel_stride_{0};
+ size_t output_pixel_stride_{0};
+ uint32_t pooling_height_{1};
+ uint32_t pooling_width_{1};
+ uint32_t stride_height_{1};
+ uint32_t stride_width_{1};
+ size_t next_input_height_{0};
+ size_t next_input_width_{0};
+ size_t next_batch_size_{0};
+ float input_scale_{1.0f};
+ float output_scale_{1.0f};
+ uint8_t input_zero_point_{121};
+ uint8_t output_zero_point_{133};
+ uint8_t qmin_{0};
+ uint8_t qmax_{255};
+ size_t iterations_{1};
+};
